Perancangan Web Marketplace Toko Sepatu Akshara.co dengan Sistem Rekomendasi Menggunakan Perhitungan Algoritma Apriori

In this advanced period, shoes arsse an essential requirement for practically all circles, a great deal of Online business organizations are arising and filling quickly in Indonesia, in addition to the quantity of dynamic web clients in Indonesia is expanding quickly from one year to another. Making Online business organizations contend to explore different techniques as far as advertising to draw in more individuals to purchase the items they offer to get by in the online businss market rivalry in this country, one illustration of a promoting methodology to draw in and increment public premium buys is the execution of the merchandise proposal framework in Online business. Consequently, in this investigation, an electronic Web based business will be made that can help Internet business organizations anticipate purchaser premium in a thing and afterward prescribe it to draw in more purchasers who come. This Web based business utilizes the Apriori Calculation way to deal with get more exactness in the information handling measure. The outcomes acquired from that examination are the making electronic Web based business by executing the suggestion technique showed on the "Akshara.co" framework include

Studi Komparasi Metode Machine Learning untuk Klasifikasi Citra Huruf Vokal Hiragana

Japanese is one of the most difficult languages to understand and read. Japanese writing that does not use the alphabet is the reason for the difficulty of the Japanese language to read. There are three types of Japanese, namely kanji, katakana, and hiragana. Hiragana letters are the most commonly used type of writing. In addition, hiragana has a cursive nature, so each person's writing will be different. Machine learning methods can be used to read Japanese letters by recognizing the image of the letters. The Japanese letters that are used in this study are hiragana vowels. This study focuses on conducting a comparative study of machine learning methods for the image classification of Japanese letters. The machine learning methods that were successfully compared are Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbor. The results of the comparative study show that the K-Nearest Neighbor method is the best method for image classification of hiragana vowels. K-Nearest Neighbor gets an accuracy of 89.4% with a low error rate.

Aplikasi Mobile Your Job MBTI (Myers-Briggs Indicator) Menggunakan Algoritma Fisher-Yates Shuffle

Work is something that everyone will do. This is because by working we will earn money that can make us able to fulfill our needs. But sometimes there are still quite a lot of people out there who don't even know what job is right for them. Therefore the author designed an application called Your Job based on Android, this application will provide suitable job recommendations based on the person's personality. In this study using the fisher-yates shuffle algorithm. Fisher-Yates shuffle algorithm can be applied to randomization of questions. The design of this application also uses the MBTI (Myers-Briggs Indicator) method to make it easier to determine a person's personality. After doing it to several people about this application. They gave a very good response, it is certain that the fisher-ystes shuffle algorithm runs well in randomizing the questions and using the MBTI method the accuracy level is almost 100% accurate.

Fuzzy Neural Network (FNN) Pada Proses Identifikasi Penyakit ISPA

ISPA is a disease that can affect anyone from children, adolescents, adults, and even the elderly. The causes experienced by sufferers of this disease are quite simple, such as fever, runny nose, and cough. The discussion in this paper describes the process of ISPA disease identification by developing a Fuzzy Neural Network (FNN) model. The process will be optimized using Fuzzy Logic to form rules for the diagnostic process, then proceed with an Artificial Neural Network (ANN). This model can maximize the performance of ANN in the identification process so that the output given is quite precise and accurate. The results provided by Fuzzy Logic can describe the clarity of the rules in diagnosis by presenting several rules (rules) that are presented from the Fuzzyfication process to the Defuzzyfication process. The output obtained from the ANN process also shows quite perfect results with an average error value based on MSE of 0.00912 and accuracy value of 91.96%. With these results, it can be stated that the FNN model can be used in the ISPA diagnosis process so that the presentation of this paper aims to provide an alternative in the identification process

Pengenalan Pola Angka Menggunakan Pendekatan Optimisasi Sistem Kekebalan Buatan (Artificial Immune System)

The general election in Indonesia itself still experiences technical and non-technical problems where the technical problems occur in the recapitulation of votes from sheet C1 which are still incorrectly inputted and done manually. The problem occurred with the difference in the uploaded C1 data and the data in the KPU Situng and the C1 sheet uploaded was blurry, unclear, sheet C1 which was crossed out or folded in the KPU Situng. The purpose of this research is to reduce errors in data input and change the work that is done manually to the system, create a number pattern recognition system using an Artificial Immune System optimization approach, test and analyze the work of the system by taking into account the level of accuracy, preciseness and speed in recognize number patterns. The system created to applies an artificial immune system optimization approach with the Artificial Immune System using the Randomized Real-Valued Negative Selection Algorithm algorithm.

IoT-Based Kobela Teaching Aid for Mathematics Learning Multiplication and Division Materials for Grade II Elementary School Students

Kotak Belajar Ajaib (Kobela) is props for elementary school math class II which can help learn to calculate multiplication and division. Based on research conducted by Sugeng Harnanto, Kobela can improve concentration, increase creativity and student learning outcomes. This tool has been tested in low-grade learning and extracurricular learning activities. The average student success in learning without using teaching aid is 54.56 (56.77%), after using teaching aid the average learning success rate reaches 90.52 (94.19%). The level of mastery learning for Basic Competencies: 3.1 Doing Multiplication of Two Numbers have increased by 37.42. In previous studies, the application of Kobela teaching aid in all learning activities was still manual-based. Potential or opportunities for development, especially for reading assessments and automatic data storage are possible to be achieved by implementing the Internet of Things (IoT). In this study, Kobela was built which implements IoT technology for reading, assessment, and recording based on learning activities. Then evaluate the system by testing the functionality of all the learning activities. From the test results, it was found that the system was running 100% by the specified function. The results of system performance testing in terms of sensor readings are on average 3 seconds with 8 Watt room lighting conditions and the average value of the assessment accuracy is 84.

Analisa Pola Penjualan Produk Sepeda Motor Yamaha Menggunakan Metode Algoritma Apriori

The development of automotive industry in Indonesia can be classifiedas very rapid and annually increasing, causing highly competitive circumstances because many companies provide various types of motorcycle brands with quality and competitive prices. The company must create a marketing strategy pattern that can increase the level of sales efficiency of Yamaha motorcycle products. To overcome this problem, a strategy that can help increasing sales of motorcycle products is needed, in which by utilizing sales data owned by the company. Data mining can be used to process company sales data by looking for association rules with apriori algorithm on motorcycle product variables. From the results of the association rule analysis on sales data, with a minimum support of 30% and a minimum confidence of 75% can produce 3 rules with 3 products that are most in demand by consumers, namely the NEW MIOM3 CW, NEWAEROX155VVA and N-MAX, by knowing the most selling products, the company can add the most selling product supply and develop a marketing strategy to market the products with other products by examining the comparative advantage of the most sold products over the other products.

Prediksi Persediaan Perlengkapan Hewan Peliharaan Pada Toko Poopy Cat Store Menggunakan Algoritma Apriori

Pets such as tame animals of various types such as cats, dogs, rabbits and others are one of the pleasures for animal lovers in having a desire to meet the needs and protect the animal from everything, difficulty in predicting the tendency of the breed. the goods to be purchased by consumers make shop owners often run out of items that are needed by consumers, this is because buyers do not make transactions and can reduce profit income to the store so it is necessary to extract information on data on buying and selling data or transaction data, in the application of extracting information using data mining methods with the APRIORI algorithm approach which is able to assist in finding out items of pet equipment from the number of sales, the results obtained from using this algorithm show the combination of the most frequent purchases carried out simultaneously on the supply of pet equipment so that it shows items that need to be stocked up more, the results obtained meet the previously set support and confidence values of 25% and 50%, the results obtained by 3 items Bolt 10 gr, Cage, Bowl get the highest value of 65%

Metode Natural Feature Tracking dan Fast Corner Detection dengan Teknik Virtual Button Pada Aplikasi Sistem Pernafasan Manusia

At this time the development of information and communication technology has developed very rapidly. As in smartphones, the technology contained in smartphones today has an important role for human activities. The technology that is currently being implemented on smartphones is Augmented Reality. Augmented reality can be used in all fields of human activity, one of which is in the field of education. The purpose of this research is to develop an educational media about the organs of the human respiratory system that is interactive and can be understood by users such as students. In this study, 2 methods were used, namely Fast Corner Detection and Natural Feature Tracking Methods to read a marker which would later bring up information and also use the virtual button technique in the application. In testing using 5 smartphones. The results of testing at an angle of 60o and 90o the five smartphones can detect markers. The distance test has different results from the smallest having a minimum distance of ±70 cm to a maximum distance of ±150 cm. In tests based on light intensity, the five smartphones can detect markers at light intensities above 5 lux

Deep Learning on Game Addiction Detection Based on Electroencephalogram

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence.